Bibliographic record
Abstract
Trade negotiations between the European Union (EU) and Australia and New Zealand (NZ) provide the opportunity to revisit the ongoing clash between EU and New World ( United States of America (USA), Canada, Australia, NZ etc.) countries over geographical indications (GIs). Since the EU-Canada negotiations, the EU has increased its GI demands and the Australia and NZ negotiations provide the first opportunity to assess these. NZ has agreed to privileges that exceed those in the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) and has fewer safeguards for NZ producers than were achieved in Canada. The EU’s GI demands to Australia are scrutinized in terms of competition, rule of law and consumer information criteria, providing a basis for considering how Australia should respond. A particular focus is the problematic issues raised in the demand that specific GI names be listed in the treaty without proper review and opposition procedures. Questions are also raised about the accuracy of EU GI labels and the relative merit of EU GI policy compared to certification mark GIs to promote regional development. On this basis it is suggested that Australia should reject a number of the EU’s GI demands as these lead to approving product labels which are deceptive for consumers Geographical indications, European Union, trade policy, regional development, consumer Information, food labelling, intellectual property
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".